Method of removing tissue from food product

ABSTRACT

A method for identifying and removing tissue from a food product that includes generating a three-dimensional model of a food product using a scanner and mapping the three-dimensional model onto the food product. The method also includes scanning the food product such that cross-sectional scanning images are generated based on the model, and, for each cross-sectional scanning image, determining a maximum thickness of the model and identifying a corresponding estimated tissue point, by using an identification method based on suitable characteristics of the food product model. The method includes fitting a curve to the estimated tissue points and generating a cut path based on the fitted curve, wherein the cut path defines an area of unwanted tissue that includes the estimated tissue points. The method further includes cutting the food product along the cut path, thereby, removing the area of unwanted tissue.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/597,450, filed on Oct. 9, 2019, which claims priority from U.S.Provisional Application No. 62/743,807, filed on Oct. 10, 2018, theentirety of which are each hereby fully incorporated by referenceherein.

FIELD OF INVENTION

The present invention relates to a food processing system and a methodfor identifying and removing undesirable tissues from food products.More particularly, certain embodiments of the present invention relateto a system and method for identifying and removing tough tissues, suchas bones, cartilage, and fat, in large-scale processed foods, such asfish or beef.

BACKGROUND OF INVENTION

In the industry of high-volume production of food products, it isdesirable to efficiently remove undesirable portions of food products,for example, fish and beef, before sending the food products to befried, battered, or otherwise processed. The undesirable portions of thefood products may include bones, cartilage, and fat. In some instances,for example, many bones are easily identifiable because the bones areeither large or visible to the naked eye, and, thus, can be removedefficiently from the food product. In other instances, small bones orbones embedded in the food product are more difficult to locate suchthat they cannot be quickly removed.

Small bones are common in food products, like fish. Fish, such as whitefish or salmon, have pin bones, which are short, curved bones thatattach to the spinal column. When a fish is filleted and the head of thefish removed, the pin bones may be hidden under the flesh.

Historically, the process of identifying and removing hidden pin bonesinvolved manual identification and removal. A person on the filletingline would be able to see the spinal column on the fillet as a series ofdots or bumps extending from the spinal column. The person cutting wouldthen run their finger along the spinal column from where the head wascut off, and feel which vertebrae still had pin bones attached. Theperson would then cut away the portion of the fish containing theremaining pin bones. This manual process of removing pin bones from fishfillets would slow down the total throughput of processed fish and,therefore, was inefficient. Additionally, manual cutting is inconsistentoften either removing too much meat or not removing all the pin bones.As such, it would be desirable to provide an automated process foridentifying and accurately removing pin bones from fish fillets.

More recently, some companies have recognized that automaticallyremoving bones from food products is desirable to enhance operatingefficiencies. However, there remain several drawbacks to currentprocesses. For example, it is known that bones can be easily identifiedusing x-ray machines or scanners. As such, some processes haveimplemented x-ray technology into their food processing systems toidentify bones in food products, including pin bones in fish. While anx-ray scanner can locate bones and their orientation with greataccuracy, implementing x-ray scanners on food processing lines can beexpensive. It would therefore be desirable to provide a bone locationprocess and system without the added cost of implementing andmaintaining expensive x-ray equipment.

Using computer vision alone (unaided by other techniques) to detectfeatures on the surface of the fish is another possible technique. Suchimaging techniques, however, have limitations, including requiring aperson with sufficient knowledge and experience in the processing ofparticular food products to sift through the many images. Additionally,current computer systems have difficulty identifying common patterns,like a series of white dots, that correlate to pin bone locations ordistinguishing between other patterns. Thereby, while estimating pinbone locations solely using imaging is desirable, it is difficult to doreliably with the state of current technology. It would, however, bedesirable if the estimation of pin bones could be done completely by acomputer system without human intervention.

Further, known mechanical techniques for removing pin bones, like vacuumremoval, are generally slower than pin bone removal fordigitally-processed food products. Mechanical pin bone removalprocessing methods typically include an additional station for manuallyverifying removal of the pin bones or removing the remaining pin bones.As such, it is desirable to design a pin bone removal system and methodthat does not require manual verification.

Further limitations and disadvantages of conventional, traditional, andproposed approaches will become apparent to one of skill in the art,through comparison of such systems and methods with the presentinvention as set forth in the remainder of the present application withreference to the drawings.

SUMMARY

A first aspect of the present invention regards a method for identifyingand removing tissue from a food product that includes generating athree-dimensional model of a food product using a scanner and mappingthe three-dimensional model onto the food product. The method alsoincludes scanning the food product such that cross-sectional scanningimages are generated based on the three-dimensional model, and, for eachcross-sectional scanning image, determining a maximum thickness of thefood product based on the three-dimensional model and identifying acorresponding estimated tissue point, using an identification methodselected from the group consisting of: (a) wherein a thickness of thecross-sectional scanning image of the three-dimensional model is atleast a predetermined percentage of the maximum thickness on the foodproduct, and (b) wherein the point is selected as being on the ventralside of the point of maximal thickness and a distance from the point ofmaximal thickness, where the distance can be customized based on aparticular size of the model. The method also includes fitting a curveto the estimated tissue points and generating a cut path based on thefitted curve, wherein the cut path defines an area of unwanted tissuethat includes the estimated tissue points. The method further includescutting the food product along the cut path, thereby, removing the areaof unwanted tissue.

A second aspect of the present invention regards a system for removingtissue from a food product that includes a scanner for generating athree-dimensional model of the food product, wherein the scanner scansthe food product such that one or more cross-sectional scanning imagesare generated. The system also includes a processor for mapping thethree-dimensional model onto the food product, scanning the food productsuch that cross-sectional scanning images are generated based on thethree-dimensional model, and, for each cross-sectional scanning image,the processor determines a maximum thickness of the food product basedon the three-dimensional model and identifies a corresponding estimatedtissue point, by using an identification method selected from the groupconsisting of: (a) wherein a thickness of the cross-sectional scanningimage of the three-dimensional model is at least a predeterminedpercentage of the maximum thickness on the food product, and (b) whereinthe point is selected as being on the ventral side of the point ofmaximal thickness and a distance from the point of maximal thickness,where the distance can be customized based on a particular size of themodel. The processor also fits a curve to the estimated tissue pointsand generates a cut path based on the fitted curve, wherein the cut pathdefines an area of unwanted tissue that includes the estimated tissuepoints. The system further includes a cutting assembly for cutting thefood product along the cut path, thereby, removing the area of unwantedtissue.

A third aspect of the present invention regards a computer-readablemedium for executing instructions to perform a method for identifyingand removing tissue from a food product, including, mapping athree-dimensional model onto a food product and scanning the foodproduct such that cross-sectional scanning images are generated based onthe three-dimensional model. The method executed by thecomputer-readable medium includes determining, for each cross-sectionalscanning image, a maximum thickness of the food product based on thethree-dimensional model and identifying a corresponding estimated tissuepoint, by using an identification method selected from the groupconsisting of: (a) wherein a thickness of the cross-sectional scanningimage of the three-dimensional model is at least a predeterminedpercentage of the maximum thickness on the food product, and (b) whereinthe point is selected as being on the ventral side of the point ofmaximal thickness and a distance from the point of maximal thickness,where the distance can be customized based on a particular size of themodel. The method executed by the computer-readable medium furtherincludes fitting a curve to the estimated tissue points and generates acut path based on the fitted curve, wherein the cut path defines an areaof unwanted tissue including the estimated tissue points. The methodexecuted by the computer-readable medium also involves controlling acutting assembly for cutting the food product along the cut path,thereby, removing the area of unwanted tissue.

One or more aspects of the present invention provide the advantage ofincorporating three-dimensional modelling into food processing such thatthe process for estimating the location of undesirable tissue andremoving such tissue is improved.

One or more aspects of the present invention provide the advantage ofscanning using visible light, which is cheaper than X-Ray imaging andfaster than current mechanical techniques.

These and other advantages and novel features of the present invention,as well as details of an illustrated embodiment thereof, will be morefully understood from the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a system representing an embodiment of asystem for identifying and removing tissue from a food product, inaccordance with the present invention.

FIG. 2 is a flow diagram representing an embodiment of a method foridentifying and removing tissue from a food product, in accordance withthe present invention.

FIG. 3A is a top view of a possible fish fillet to be processed usingthe method of FIG. 2.

FIG. 3B is a top view of a possible fish fillet to be processed usingthe method of FIG. 2 offset from the direction of product flow.

FIG. 4 is a side-view of a possible embodiment of a scanner for use withthe system in FIG. 1.

FIG. 5 is a cross-sectional view of an array of images taken by thescanner of FIG. 4

FIG. 6 is a top, close-up view of the fish fillet of FIGS. 3A-B with thepin bones identified by circles.

FIG. 7 is a top view of an image of the fish fillet of FIGS. 3A-B withscan lines generated from a three-dimensional model mapped to thefillet.

FIG. 8 is a top, close-up view of the image of FIG. 7.

FIG. 9 is a top view of the image of FIG. 7 showing the projected centerof mass.

FIG. 10A is an embodiment of a cross-sectional view of athree-dimensional model of a food product.

FIG. 10B is an alternative embodiment of a cross-sectional view of athree-dimensional model of a food product.

FIG. 11 is a top view of the grayscale image of the fish fillet of FIGS.3A-B.

FIG. 12 is a top view of the fish fillet of FIGS. 3A-B having tissueremoved in accordance with the method of FIG. 2.

DETAILED DESCRIPTION

As shown in FIG. 1, the system 1000 includes an input side 1010 wherethe food product enters the system 1000. The food product flows in thex-direction as shown in FIG. 1. In some embodiments, the system 1000includes a conveyor 1012 for carrying the food product through thesystem 1000. In the present invention, the conveyor 1012 passes underthe scanner 1020 where the scanner takes a series of images to generatea three-dimensional model of the food product. The system 1000 furtherincludes a computer system 1025, which communicates with scanner 1020.Computer system 1025 includes a processor 1030 for performing the methodof the present invention, as will be described in further detail below.Further, system 1000 includes the cutting assembly 1040 for cutting thefood product and removing the undesirable tissue.

In some embodiments, the infeed conveyor 1012 may extend through thesystem 1000. However, as shown in FIG. 1, the system 1000 may include anoutfeed conveyor 1014 placed adjacent to the end of infeed conveyor 1012that is positioned at either the entrance or exit end of the scanner1020. The outfeed conveyor 1014 may extend to an exit end of the cuttingassembly 1040 The food product may be transferred from the infeedconveyor 1012 to outfeed conveyor 1014 for the cutting step of theinventive process because outfeed conveyor 1014 may be formed from adurable material, like metal, such that it can withstand existingmethods of industrial cutting, such as high-pressured water jetsperformed within cutting assembly 1040. The present invention may alsoinclude an additional conveyor 1016 positioned at the exit end of thecutting assembly 1040 and used for discarding the removed tissue fromthe system 1000 or transporting the cut food product to another foodprocessing system, such as baking, battering, or breading.

In some embodiments, computer system 1025 may incorporate the processingcapabilities of the scanner 1020 such that computer system 1025 operatewithin a single computer. Similarly, the computer system 1025 mayincorporate the control functionality of the cutting assembly 1040, and,thereby, it is contemplated that a robust computer system 1025 and/orprocessor 1030 be used with the system 1000. FIG. 2 illustrates aprocess performed by system 1000 for identifying and removingundesirable food products from a food product, like fish, beef, chicken,or any other food product having tough tissues.

In the present invention, to assure the food product can be properlyanalyzed and cut, as will be described in further detail below, the foodproduct should be oriented on the conveyor 1012 such that scanner 1020,processor 1030, and cutting assembly 1040 operate with reference to thesame coordinate system. As shown in FIG. 3A, the fish fillet is orientedsuch that the skin-side of the fillet (i.e. the underside of the fish,not shown) lies against the conveyor 1012 and the head of the filletfalls in the direction of the product flow along the x-axis. In otherembodiments, as shown in FIG. 3B, the food product can be oriented inany direction relative to the flow of food in an axis, x′-y′. In suchembodiments, the processor 1030 may require an additional step oftransforming alternative orthogonal coordinates X′, Y′, angled by anangle, Θ, relative to the X-Y coordinate system. For example, if thehead of the fillet is placed on the conveyor 1012 at an angle Θ=45°relative to the direction of product flow (i.e. the x-axis), as shown inFIG. 3A, the processor 1030 will have to rotate the x-y coordinates by45° to coincide with the orientation of the fish fillet to analyze thethree-dimensional model, as will be described in further detail below.

Alternatively, it is contemplated that the orientation of the foodproduct relative to the system 1000 need not be uniform. In fact, thesystem 1000 may be able to process the food product regardless of itsorientation. In such a system 1000, the scanner 1020 and/or computersystem 1025 may be able to process the food product based oncharacteristics unique to the processed food product. For example, pinbones in white fish are often easily identifiable if the dorsal neckpoint and dorsal edge can be identified, as will be described below infurther detail. As such, once these unique characteristics areidentified by the scanner 1020 and/or computer system 1025, the whitefish can be processed on a coordinate system unique to that fish fillet.

After the food products enter the system 1000, the scanner 1020generates a three-dimensional model of the food product, shown in FIG. 2as step 1022. The scanner 1020 may include a digital camera that takes aseries of pictures of the food product at set intervals of travel, forexample, every one millimeter. The scanner 1020 may include a digitalcamera that takes a series of images of a laser stripe shining on thefood product at set intervals of travel, such as one millimeter, asshown in FIG. 4. The scanner 1020 may also incorporate other knownmethods of imaging to generate a three-dimensional model 1050 (see FIGS.7-9) of the food product. The series of images taken by the scanner 1020are sent to computer system 1025 so the images can be compiled togenerate a three-dimensional model 1050 of the food product. In otherembodiments, the images can be taken at irregular intervals, or smalleror larger increments depending on the preference of the user and thedesired accuracy of the model 1050. Accordingly, the fewer images thatare taken the more interpolation between the images is required togenerate the three-dimensional model 1050.

Along with generating a three-dimensional model 1050 of the foodproduct, the computer system 1025 assigns each image a coordinate valueto record the model's position relative to the conveyor 1012. Thecoordinate values are a set of three-dimensional coordinates that existin the x-y coordinate plane shown in FIG. 3 with the z-axis (notshown—coming out of the page perpendicular to the x-y axis). In someembodiments, the computer system 1025 implements a camera calibrationmethod that establishes a fixed relationship between the images taken bythe camera 1023 and the location of the food product on the infeedconveyor 1012. Such a calibration allows the three-dimensional model tobe built, processed, and analyzed without reference to the real-worldsystem 1000. In other embodiments, other suitable approaches foridentifying points in three-dimensional space may be used, including,for example, a polar coordinate system.

In the present embodiment, the food product is scanned as it approachesthe light emitting devices of the scanner 1020 located above theconveyor 1020, as shown in FIG. 4. The scanner 1020 includes a digitalcamera 1023, a laser 1024, and a system of mirrors 1027 mounted onto aframe. The laser 1024 emits a laser stripe spanning the infeed conveyor1012 and the food product thereon. The camera 1023 uses a set of mirrors1027 to view the laser from both the front and back of the food product.The camera 1023 takes images at a set rate, for example, 200frames/second. As shown in FIG. 5, the front and back images, takentogether, are combined at each location to generate a compiledcross-sectional image of the food product. The computer system 1025 thenstitches successive compiled images to create the three-dimensionalmodel 1050 of the food product. In other embodiments, it is possible tohave more than one scanner 1020 to scan incoming food product. Thescanners may be located at any suitable point along the conveyor 1012 solong as the scanner or set of scanners 1020 can assess the food productin three dimensions.

Once a three-dimensional model 1050 of the food product is generated,the processor 1030 performs a set of functions, shown in FIG. 2 as steps1030(a)-1030(f), based on the three-dimensional model 1050. Onefunction, as shown in FIG. 7, includes the processor 1030 mapping scanlines 1060 to the three-dimensional model 1050. For example, whenmapping potential scan lines to a model of a fish fillet, it isdesirable to place the scan lines 1060 relative to the dorsal side, asidentified in FIG. 3, and extending perpendicularly therefrom, as shownin FIG. 7. To effectively place the scans relative to the dorsal side ofthe fillet, the end of the dorsal side nearest to the removed fish headmust be identified because, as discussed previously, pin bones extendingfrom a fish spinal column are unwanted tissue. If the fish fillet isdesirably oriented, as discussed previously, the leading edge of thefish is the end point of the dorsal side, called the dorsal neck point1055. For other orientations where the leading edge of the fish filletor other food product is not a useful location on the model, it iscontemplated that the process can be modified to account for suchcircumstances. In other embodiments, any suitable identifiablecharacteristic unique to a food product can be used to place the scans.

After identifying the starting point for the scan lines 1060, like thedorsal neck point 1055, the curvature of the dorsal side of the fishfillet is approximated using a regression analysis. By approximating theedge, or other reference point, of the food product, the scan lines 1060can be placed at any angle relative to the regression line. In thepresent embodiment, the scan lines 1060 are placed perpendicularly tothe regression line and extend across the width of the fillet as shownin FIGS. 7-9.

Once a reference point (or series of reference points) is identified,for example, the dorsal neck point 1055 and/or dorsal side for a fishfillet, the processor 1030 can place scan lines 1060 across thethree-dimensional model 1050. In the present embodiment, the scan lines1060 are placed at intervals of two millimeters; however, any othersuitable interval may be used based on user preferences. Finer orcoarser intervals may also be desirable depending on the type of foodproduct being processed. For example, pin bones are small and thin, and,thereby, require a fine scale to accurately estimate the location ofeach bone. Therefore, based on the density or size of the unwantedtissue to be identified, the intervals may be adjustable.

In the present embodiment shown in FIGS. 7 and 8, the scan lines 1060extend over a portion of the model 1050. The scan lines 1060 begin atthe head of the fish fillet and extend toward to the tail of the fishfillet model 1050. The scan lines 1060 cease at the desired depth of thecut, D_(cut), as specified by a user and/or the type of food product andlocation of the unwanted tissue.

As shown in FIG. 9, the user-specified depth at which to end the scanlines 1060 is, for example, approximately 70% of the distance betweenthe dorsal neck point 1055 and the center of mass 1051. Thisapproximation for ceasing scan lines 1060 is determined by finding thecenter of mass 1051 of the model 1050 and projecting the center of mass1051 onto the dorsal side of the model 1050. A straight line 1052 isgenerated between the projected center of mass on the dorsal side andthe dorsal neck point 1055. The scan lines 1060 end at theuser-specified percentage of the depth, D_(cut), of the line 1052,approximately 70%.

In other embodiments, the desired depth of the cut, D_(cut), may varyfor different food products and may be measured from any suitableidentifiable characteristic in the food product model. For instance,certain cuts of steak, like top sirloins, may have a thin layer of fatextending along the length of the side of the beef. Because the layer offat is thin, the depth of the cut, may only need to be 5% of thedistance between the side of the beef model and the center of mass.Thus, scan lines would only need to be placed over the corresponding 5%of the model to be cut.

In other embodiments, it is contemplated that the scan lines begin andend at any location on the food product model 1050, including extendingacross the entire length of the model. For example, if the model was ofa T-bone cut of steak, the scans would ideally extend over the portionof the cut containing the T-bone such that cut paths can be generated,as will be described in further detail below.

After the scan lines 1060 are mapped to the model 1050, the processor1030 generates cross-sectional scanning images, I_(i), where i equals 1,2, 3, . . . n and n is an integer greater than 1, where each scanningimage corresponds to a scan line 1060. As shown in FIGS. 10A and 10B,for each of the cross-sectional images, the processor 1030 determinesvarious characteristics of the food product model at that location,including the maximum thickness of the model, T_(max,i) the change inthickness across the model, the width of the model, and thethree-dimensional coordinates of the scan. Maximum thickness may beinterpreted to mean above a certain percentage threshold, like 95%,because noise may obscure the accuracy of the model. In otherembodiments, the processor 1030 may calculate other suitablecharacteristics related to processing a particular food product.

In the present invention, once relevant characteristics are calculatedfor each cross-sectional scanning image, the processor 1030 can thenidentify estimated tissue points, TP_(i), corresponding to points ofunwanted tissue, like bones. Often in the food processing industry, thesame cuts of a certain food product may share the same unwanted tissue,for example, fish fillets can contain pin bones. Therefore, the locationof such unwanted tissue can be estimated with reasonable accuracy basedon the common cut, i.e. the location of the unwanted tissue issubstantially common across all cuts of a food product. For example, asshown in FIG. 10A, pin bones in fish fillets are located closer to theventral side of the fish fillet, rather than the dorsal side, atapproximately 75% of the maximum thickness of the cross-section of thefillet. Thus, the processor 1030 identifies an estimated tissue point,TP_(i), at 75% of the maximum thickness of each cross-sectional scanningimage, I_(i), along the scan line. In an alternative embodiment, shownin FIG. 10B, the estimated tissue points, TP_(i), are located at 70% ofthe maximum thickness of each cross-sectional scanning image, I_(i),along the scan line.

Likewise, the estimated tissue points, TP_(i), may be estimated based onbeing on the ventral side of the fillet model and a correspondingdistance from the point maximum thickness. That would allow a distanceto be calculated customized based on a particular size of the filletmodel. In other embodiments, the estimated tissue points, TP_(i), may beestimated based on the distance from the spine of the fish, or thethickest part of the model, because the pin bones typically follow acurve similar to that of the maximum thickness of the model 1050.

In some embodiments, as shown in FIG. 11, the processor 1030 mayincorporate additional inputs from a grayscale image scanner to verifythe estimated tissue points identified using relevant characteristics ofthe food product. A grayscale image can be captured from the scanner1020 by measuring the intensity of the laser 1024 in the same imagescaptured by digital camera 1023, discussed above. Hence, scanner 1020can use laser 1024 and digital camera 1023 to not only measure positionto form a three-dimensional model, but also measure intensity to form agrayscale model. The pin bones in fish fillets may appear in a grayscaleimage as a series of white dots extending from the head side of thefish. It is contemplated that processor 1030 may be programmed toidentify such a pattern in a grayscale image of the fish fillet andverify the location of the estimated tissue points on thethree-dimensional model 1050. A grayscale model may verify the locationof the tissue points in various ways. For example, the processor 1030may implement a confidence score based on the brightness of the suspectdots, the signal to noise ratio, and the spacing and positioning of thedots. If the confidence score is within a suitable range, the filletwill proceed to the cutting assembly 1040. If the confidence score isoutside of a suitable range, the fillet model may be reassessed toverify the estimated pin bone locations. Alternatively, the model 1050and grayscale model may be used in conjunction to devise a cut path tosatisfy each model individually. Verifying the location of the estimatedtissue points can result in fitting a more accurate curve, as will bedescribed in detail below, and, thus, capturing the unwanted tissue.Grayscale imaging can be particularly useful for identifying and/orverifying the location of fatty tissues in food products, like beef.

Other combinations of image processing scanners may be used inconjunction with the scanner 1020 and grayscale image scanner to verifythe location of unwanted tissues in food products, including x-rayscanners, particularly for locating bones.

Once the processor 1030 generates an estimated tissue point for eachcross-sectional scanning image, the processor 1030 fits a curve 1065 tothe estimated tissue points based on their associated coordinates in thethree-dimensional model 1050. In some embodiments, the fitted curve 1065may be adjusted or approximated based on a subsection of the estimatedtissue points, such as by excluding outliers or points outside aspecified range. The curve 1065 can be fitted using any suitablemathematical methodology, including, for example, polynomial regressionand polynomial interpolation.

Alternatively, the fitted curve 1065 could be iteratively adjusted orapproximated based on another feature of the food product. For example,the points of maximum thickness in the model may follow a similar curveof the estimated pin bone locations such that those points may form acurve model to adjust the fitted curve 1065. That is, the points ofmaximum thickness generally follow a parallel curve to that of the pinbones.

In some embodiments, an iterative approach to mapping scan lines 1060may be implemented. For example, if scan lines 1060 are initially placedperpendicular to the regression line approximating the dorsal side ofthe fish on the model 1050 such that the scan lines are not evenlyspaced across the fitted curve 1065, the processor 1030 may adjust thelines 1060 to create even intervals, or denser intervals, over thefitted curve 1065. Such a recalculation of the scan lines 1060 allowsfor the area of unwanted tissue to be more closely estimated. Theintervals can be adjusted by changing the scan angle at which the scanlines 1060 meet the regression line of the dorsal side of the model. Forexample, instead of extending perpendicularly from the dorsal sideregression line, as previously described, the scan lines 1060 maydeviate from 90° accommodate the change in intervals.

Based on the fitted curve 1065, the processor 1030 generates a cut path1075 to remove an area of unwanted tissue from the food product,thereby, capturing the fitted curve 1065. In the present invention, thetrajectory of the cut path 1075 depends on user-specified inputs,including the desired depth of the cut and width of the removal. Asshown in FIG. 9, the cut path 1075 is triangular or V-shaped such thatthe cut path 1075 captures the fitted curve 1065 up to theuser-specified cut depth, 70%, and exits the food product at auser-specified width such as to capture an unwanted area of tissue. Thefitted curve 1065 serves as the starting position of an initial cutpath. The fitted curve 1065 is then rotated by a user-specified angletoward the dorsal side of the fish fillet, as shown in FIG. 9, thereby,forming a dorsal side of the cut path 1076. Similarly, the fitted curve1065 is rotated by a user-specified angle toward the ventral side of thefish fillet. As shown in FIG. 9, thereby, forming a ventral side of thecut path 1077. The ventral side cut path 1077 and dorsal side cut path1076 together form a triangular-shaped cut. A user may also specify thewidth of the tip cut path 1078 such that the cut path 1075 resembles atrapezoid rather than a triangle. In other embodiments, any suitableuser-specified parameters may be incorporated into the generation of thecut path 1075 depending on the cut and type of food product beingprocessed. It is also contemplated that the cut path be modified tomatch any curvature in the fitted curve, as previously described.

After generating the cut path 1075, the food product is carried to thecutting assembly 1040 by the cutting conveyor 1014. The cutting assembly1070 executes step 1071 by cutting away the unwanted tissues accordingto the generated cut path 1075, as shown in FIG. 12. As previouslydescribed, the cut path 1075 is generated by the processor 1030 in acoordinate system corresponding to the “real-world” position of the foodproduct relative to system 1000, and, thus, the cutting assembly 1040can simply cut the food product according to the cut path 1075.

In some embodiments, the cutting assembly 1040 includes high-pressuredwater jets for cutting the food product or any other suitable cuttingmechanism known in the food processing industry, like knives or blades.In other embodiments, a mechanical method for pulling out pin bones,such as vacuums, may be used. The cutting assembly 1040 may also becapable of cutting the food product at a user-specified angle relativeto the z-axis (not shown) perpendicular to the x-y axis shown in FIGS.3A-B. Cutting the food product at an angle can be efficient in someinstances to limit to the amount of neighboring, desirable food productremoved from the food product. Further, a cutting assembly 1040 withadjustable cutting heads or a similar means for adjusting cutting anglescan more easily remove irregularly-shaped unwanted tissues in foodproducts.

In some embodiments, the computer system 1025 includes a graphical userinterface 1026 (GUI). GUI 1026 allows a user to provide user-specifiedcut path characteristics or parameters for defining the cut path 1075.In the present invention, a user may input the angle of the cut path,the width of the cut path, and the depth of removal of unwanted tissue.The available parameters for user specification may vary for each typeor cut of food product.

In summary, an improved method is disclosed for identifying and removingunwanted food product (e.g. bones, cartilage, and fat). The improvedmethod includes an improved, accurate method of estimating tissue. Allof the enhancements expand the functionality of the processing of foodswith unwanted tissues and increase the efficiency of identifying suchunwanted tissues.

While the invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the invention without departing from its scope.Therefore, it is intended that the invention not be limited to theparticular embodiment disclosed, but that the invention will include allembodiments falling within the scope of the appended claims.

1. A method for identifying and removing tissue from a food product,comprising: generating a three-dimensional model of a food product usinga scanner; mapping the three-dimensional model onto the food product;scanning the food product such that cross-sectional scanning images,I_(i), are generated based on the three-dimensional model, wherein i=1,2, 3, . . . n and n is an integer greater than 1; for eachcross-sectional scanning image, I_(i), determining a maximum thickness,T_(max,i), of the food product based on the three-dimensional model; foreach cross-sectional scanning image, I_(i), identifying a correspondingestimated tissue point, TP_(i), by using an identification methodselected by determining: a) a thickness of the cross-sectional scanningimage, I_(i), of the three-dimensional model is at least a predeterminedpercentage of the maximum thickness, T_(max,i), on the food product, andb) wherein the point, TP_(i), is selected as being on the ventral sideof the point of maximal thickness, T_(max,i), and a distance from thepoint of maximal thickness, T_(max,i), where the distance can becustomized based on a particular size of the model; the method furthercomprising: fitting a curve to the estimated tissue points, TP_(i) basedupon the coordinate points of the TP_(i) for each cross-sectionalscanning image, wherein any identified tissue points, TP_(i), that areoutside of a specified range are not included in the fitting of thecurve; generating a cut path based on the fitted curve, wherein the cutpath defines an area of unwanted tissue that comprises the estimatedtissue points, TP_(i); cutting the food product along the cut path,thereby, removing the area of unwanted tissue.
 2. The method of claim 1,wherein the estimated tissue point TP_(i), is calculated as a percentageof thickness and that percentage is approximately 70%.
 3. The method ofclaim 1, wherein the cut path is angled as measured from a z-axis. 4.The method of claim 3, further comprising: selecting at least onecharacteristic of the angled cut path, wherein the characteristic isselected from the group consisting of: width, cut angle, and depth. 5.The method of claim 1, further comprising: verifying the set ofestimated tissue points, TP_(i), by generating a verified image of thefood products.
 6. The method of claim 5, wherein the verified image is agrayscale image.
 7. The method of claim 1, wherein the cutting isperformed by water jets.
 8. The method of claim 1, wherein the removingof the area of unwanted tissue is performed by a vacuum or mechanically.9. A system for identifying and removing tissue from a food product,comprising: a scanner for generating a three-dimensional model of thefood product, wherein the scanner scans the food product such thatcross-sectional scanning images, I_(i), are generated, wherein i=1, 2,3, . . . n and n is an integer greater than 1; a processor for mappingthe three-dimensional model onto the food product, scanning the foodproduct such that cross-sectional scanning images, I_(i), are generatedbased on the three-dimensional model, wherein i=1, 2, 3, . . . n and nis an integer greater than 1, for each cross-sectional scanning image,I_(i), determining at least a maximum thickness, T_(max,i), of the foodproduct based on the three-dimensional model, for each cross-sectionalscanning image, I_(i), identifying a corresponding estimated tissuepoint, TP_(i), by using an identification method selected bydetermining: (a) a thickness of the cross-sectional scanning image,I_(i), of the three-dimensional model is at least a predeterminedpercentage of the maximum thickness, T_(max,i), on the food product, and(b) wherein the point, TP_(i), is selected as being on the ventral sideof the point of maximal thickness, T_(max,i), and a distance from thepoint of maximal thickness, T_(max,i), where the distance can becustomized based on a particular size of the model; fitting a curve tothe estimated tissue points, TP_(i), based upon the coordinate points ofthe TPi for each cross-sectional scanning image, wherein any identifiedtissue points, TPi that are outside of a specified range are notincluded in the fitting of the curve and generating a cut path based onthe fitted curve, wherein the cut path defines an area of unwantedtissue that comprises the estimated tissue points, TP_(i); a cuttingassembly for cutting the food product along the cut path, thereby,removing the area of unwanted tissue.
 10. The system of claim 9, whereinthe estimated tissue point TP_(i), is calculated as a percentage ofthickness and that percentage is approximately 70%.
 11. The system ofclaim 9, wherein the cut path is angled as measured from a z-axis. 12.The system of claim 11, further comprising: a graphical user interfacefor selecting at least one characteristic of the angled cut path,wherein the characteristic is selected from the group consisting of:width, cut angle, and depth.
 13. The system of claim 9, furthercomprising: an infeed conveyor for transporting the food product by thescanner.
 14. The system of claim 13, further comprising: an outfeedconveyor upon which the food product is cut, wherein the outfeedconveyor is placed adjacent to the infeed conveyor such that the foodproduct can transfer from the second end of the infeed conveyor onto theoutfeed conveyor.
 15. The system of claim 9, wherein the scannerverifies the estimated tissue points, TP_(i), prior to cutting with averified image.
 16. The system of claim 15, wherein the verified imageis a grayscale image.
 17. The system of claim 9, wherein the cuttingassembly comprises water jets.
 18. The method of claim 9, wherein thecutting assembly comprises a vacuum or mechanical removal means.
 19. Acomputer-readable medium for executing instructions regarding a methodfor identifying and removing tissue from a food product, the methodcomprising: mapping a three-dimensional model onto a food product;scanning the food product such that cross-sectional scanning images,I_(i), are generated based on the three-dimensional model, wherein i=1,2, 3, . . . n and n is an integer greater than 1; for eachcross-sectional scanning image, I_(i), determining at least a maximumthickness, T_(max,i), of the food product based on the three-dimensionalmodel; for each cross-sectional scanning image, I_(i), identifying acorresponding estimated tissue point, TP_(i), by using an identificationmethod selected by determining: (a) wherein a thickness of thecross-sectional scanning image, I_(i), of the three-dimensional model isat least a predetermined percentage of the maximum thickness, T_(max,i),on the food product, and (b) wherein the point, TP_(i), is selected asbeing on the ventral side of the point of maximal thickness, T_(max,i),and a distance from the point of maximal thickness, T_(max,i), where thedistance can be customized based on a particular size of the modelfitting a curve to the estimated tissue points, TP_(i) based upon thecoordinate points of the TPi for each cross-sectional scanning image,wherein any identified tissue points, TPi, that are outside of aspecified range are not included in the fitting of the curve, andgenerating a cut path based on the fitted curve, wherein the cut pathdefines an area of unwanted tissue that comprises the estimated tissuepoints, TP_(i); controlling a cutting assembly for cutting the foodproduct along the cut path, thereby, removing the area of unwantedtissue.
 20. The method of claim 1, further comprising the step ofdetermining whether the food product is aligned upon a conveyor in anorientation with respect to a coordinate system that is established uponthe conveyor that is expected with respect to a typical alignment of thefood product upon the conveyor, and upon determining that the foodproduct is not aligned as expected with respect to the coordinatesystem, the step of generating the three-dimensional model of the foodproduct using the scanner further comprises the step of transforming thecoordinate system by an angle θ that is equal to an angle ofmisalignment between the determined alignment of the food product andthe expected alignment of the food product upon the conveyor withrespect to the coordinate system that is established upon the conveyorthat is expected with respect to the typical alignment upon theconveyor.
 21. The method of claim 9, wherein the processor is furtherconfigured for determining whether, using the generatedthree-dimensional model of the food product by the scanner, the foodproduct is aligned upon a conveyor in an orientation with respect to acoordinate system that is established upon the conveyor that is expectedwith respect to a typical alignment of the food product upon theconveyor, and upon determining that the food product is not aligned asexpected with respect to the coordinate system, the processor is furtherconfigured to generate the three-dimensional model of the food productwith transforming the coordinate system by an angle Θ that is equal toan angle of misalignment between the determined alignment of the foodproduct and the expected alignment of the food product upon the conveyorwith respect to the coordinate system that is established upon theconveyor that is expected with respect to the typical alignment upon theconveyor.